Stream flow Simulation by Wetspa Model in Garmabrood Watershed of Mazandaran Province, Iran

2017 ◽  
Keyword(s):  
Stream Flow ◽  
Flow Simulation ◽  
Mazandaran Province ◽  
Wetspa Model

A Review on SWAT Model for Stream Flow Simulation

2013 ◽  
Vol 726-731 ◽  
pp. 3792-3798
Author(s):  
Wen Ju Zhao ◽  
Wei Sun ◽  
Zong Li Li ◽  
Yan Wei Fan ◽  
Jian Shu Song ◽  
...  
Keyword(s):  
Spatial Data ◽  
Stream Flow ◽  
Assessment Tool ◽  
Swat Model ◽  
Flow Simulation ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Rs And Gis ◽  
Model Research ◽  
Gis And Rs

SWAT (Soil and Water Assessment Tool) model is one of distributed hydrological model, based on spatial data offered by GIS and RS. This article mainly introduces the SWAT model principle, structure, and it is the application of stream flow simulation in China and other countries, then points out the deficiency existing in the process of model research. In order to service in water resources management work better, experts and scholars further research the rate constant and uncertainty of the simplification of the model parameters, and the combination of RS and GIS to use, and hydrological scale problems.

scholarly journals Evaluating the accuracy of C- and X-band weather radars and their application for stream flow simulation

2012 ◽  
Vol 15 (4) ◽  
pp. 1121-1136 ◽  
Author(s):  
N. K. Shrestha ◽  
T. Goormans ◽  
P. Willems
Keyword(s):  
Conceptual Model ◽  
Stream Flow ◽  
Flow Simulation ◽  
Winter Storms ◽  
Weather Radars ◽  
X Band ◽  
Rainfall Estimates

This paper investigates the accuracy of rainfall estimates from C- and X-band weather radars and their application for stream flow simulation. Different adjustment procedures are applied to raw radar estimates using gauge readings from a network of 12 raingauges. The stream flow is simulated for the 48.17 km2 Molenbeek/Parkbeek catchment located in the Flemish region of Belgium based on a lumped conceptual model. Results showed that raw radar estimates can be greatly improved using adjustment procedures. The gauge-radar residuals however, remain large even after adjustments. The adjusted X-band radar estimates are observed to be better estimates than corresponding C-band estimates. Their application for stream flow simulation showed that raingauges and radars can simulate spatially more uniform winter storms with almost the same accuracy, whereas differences are more evident on summer events.

Gamma‐Autoregressive Models for Stream‐Flow Simulation

1990 ◽  
Vol 116 (11) ◽  
pp. 1403-1414 ◽  
Author(s):  
Bonifacio Fernandez ◽  
Jose D. Salas
Keyword(s):  
Stream Flow ◽  
Flow Simulation ◽  
Autoregressive Models

Application of SEBAL and Markov Models for Future Stream Flow Simulation Through Remote Sensing

2010 ◽  
Vol 24 (14) ◽  
pp. 3773-3797 ◽  
Author(s):  
Chih-Da Wu ◽  
Chi-Chuan Cheng ◽  
Hann-Chung Lo ◽  
Yeong-Keung Chen
Keyword(s):  
Remote Sensing ◽  
Stream Flow ◽  
Markov Models ◽  
Flow Simulation

scholarly journals Calibration of SWAT and three data-driven models for monthly stream flow simulation in Maharlu Lake Basin

2021 ◽  
Author(s):  
Fatemeh Moazami Goudarzi ◽  
Amirpouya Sarraf ◽  
Hassan Ahmadi
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Computational Intelligence ◽  
Stream Flow ◽  
River Flow ◽  
Swat Model ◽  
Flow Simulation ◽  
Support Vector ◽  
Lake Basin ◽  
Computational Intelligence Methods

Abstract In this study, the performance of SWAT hydrological model and three computational intelligence methods used to simulate river flow are investigated. After collecting the data required for all models used, the calibration and validation stages were performed. Using the SWAT model and three methods of the Extreme Machine Learning (EML), the Support Vector Regression (SVR), and the Least Squares Support Vector Regression (LSSVR), Maharlu Lake Basin stream flow was simulated and the results obtained at Shiraz station were used for this study. A noise reduction filter was employed to improve the results from the computational intelligence methods, and SUFI-2 algorithm was used to analyze the uncertainty of the SWAT model. Finally, in order to evaluate the models developed and the SWAT model, three statistics (RMSE), (R²), and (NS) coefficient were used. The results indicated that the SWAT model and the machine learning models were generally appropriate tools for daily flow modeling, but the LSSVR model showed less errors in both learning and testing, with the coefficients NS = 0.997 and R² = 0.997 in the calibration stage and NS = 0.994 and R² = 0.994 in the validation stage, which prove their better performance compared to the other methods and the SWAT model.

A new numerical model for simulating top surface soil moisture and runoff

2018 ◽  
Vol 35 (3) ◽  
pp. 1344-1363 ◽  
Author(s):  
Jiongfeng Chen ◽  
Wan-chang Zhang
Keyword(s):  
Soil Moisture ◽  
Stream Flow ◽  
Hydrological Model ◽  
Surface Soil ◽  
Flow Simulation ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Surface Soil Moisture ◽  
Content Type ◽  
Simulation Results

PurposeThis paper aims to construct a simplified distributed hydrological model based on the surveyed watershed soil properties database.Design/methodology/approachThe new established model requires fewer parameters to be adjusted than needed by former hydrological models. However, the achieved stream-flow simulation results are similar and comparable to the classic hydrological models, such as the Xinanjiang model and the TOPMODEL.FindingsGood results show that the discharge and the top surface soil moisture can be simultaneously simulated, and that is the exclusive character of this new model. The stream-flow simulation results from two moderate hydrological watershed models show that the daily stream-flow simulation achieved the classic hydrological results shown in the TOPMODEL and Xinanjiang model. The soil moisture validation results show that the modeled watershed scale surface soil moisture has general agreement with the obtained measurements, with a root-mean-square error (RMSE) value of 0.04 (m3/m3) for one of the one-measurement sites and an averaged RMSE of 0.08 (m3/m3) over all measurements.Originality/valueIn this paper, a new simplified distributed hydrological model was constructed.

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